Probabilistic State Machines to describe emotions Happy state Ironic state Unhappy state “you are...

Probabilistic State Machines to describe Probabilistic State Machines to describe emotionsemotions

Happy state

Ironic state

Unhappy state

“you are beautiful”

/ ”Thanks for a compliment”

“you are blonde!”

/ ”I am not an idiot”

P=1

P=0.3

“you are blonde!”

/ Do you suggest I am an idiot?”

P=0.7

Human speaks to robot

Robot speaks to human (will be in italic in next slides)

Facial Behaviors of MariaFacial Behaviors of Maria

Do I look like younger than twenty three?Maria asks:Maria asks:

“yes”

“no” “no”

0.30.7

Response from Response from a human:a human:

Maria smilesMaria smilesMaria frownsMaria frowns

In word spotting mode human responses are short

Probabilistic Grammars for performancesProbabilistic Grammars for performances

Who?

What?

Where?

Speak ”Professor Perky”, blinks eyes twice

Speak “In the classroom”, shakes head

P=0.1

Speak “Was drinking wine”

P=0.1

P=0.3

P=0.5

Speak ”Professor Perky”

Speak ”Doctor Lee”

Speak “in some location”, smiles broadly

Speak “Was singing and dancing”

P=0.5 P=0.5

P=0.1 P=0.1

….

P=0.1A complete behavior of a robot described as a probabilistic (quantum entangled) machine

Human-controlled modes of Human-controlled modes of dialog/interactiondialog/interaction

Robot asks

Human teaches

Human commandsHuman asks

Robot performs

“Hello Maria”

“Thanks, I have a question”

“Thanks, I have a lesson”

“Thanks, I have a command”

“Lesson finished”

“Questioning finished”

“Command finished”

“Stop performance”

“Question”

initialization

This slide shows only human speech controls, no robot responses showing its state

The next slides will illustrate the following The next slides will illustrate the following robot behaviors:robot behaviors:

• Robot Initiated Conversation. Robot Initiated Conversation. • Human-Initiated Conversation.Human-Initiated Conversation.• Human asking.Human asking.• Human teaching.Human teaching.• Human verifying what robot learned.Human verifying what robot learned.

• Robot Performing (text, music, movements). Robot Performing (text, music, movements). • Game Playing.Game Playing.

Other robot behaviors:Other robot behaviors:

Robot-Receptionist Initiated Robot-Receptionist Initiated ConversationConversation

Robot

What can I do for you?What can I do for you?

Human

Robot asksThis represents operation mode


Robot


Human

I would like to order a I would like to order a table for twotable for two

Robot asks


Robot

Smoking or non-Smoking or non-smoking?smoking?

Human

Robot asks


Robot

Smoking or non-Smoking or non-smoking?smoking?

Human

I do not understandI do not understand

Robot asks


Robot

Do you want a table in a Do you want a table in a smoking or non-smoking smoking or non-smoking section of the restaurant? section of the restaurant?

Non-smoking section is Non-smoking section is near the terrace.near the terrace.

Human

Robot asks


Robot

Do you want a table in a Do you want a table in a smoking or non-smoking smoking or non-smoking section of the restaurant? section of the restaurant?

Non-smoking section is Non-smoking section is near the terrace.near the terrace.

Human

A table near the terrace, please

Robot asks

Human-Initiated ConversationHuman-Initiated Conversation

Robot Human

Hello Maria

Robot asks

initialization

Human-Initiated ConversationHuman-Initiated Conversation

Robot Human

Hello MariaWhat can I do for you?

Robot asks

Human-AskingHuman-Asking

Robot Human

Question

Human asksQuestionRobot asks


Robot Human

Question

Human asks

Yes, you ask a question.


Robot Human

What book wrote Lee?

Human asks



Robot Human


Human asks

I have no sure information.


Robot Human

Try to guess.

Human asks



Robot Human

Try to guess.

Human asks

Lee wrote book “Flowers”.


Robot Human

This is not true.

Human asks

Lee wrote book “Flowers”.

Human-TeachingHuman-Teaching

Robot Human

Questioning finished

Human teaches“Questioning finished” Robot asks

Human asks

Thanks, I have a lesson

Human endsHuman endsquestioningquestioning


Robot Human

Questioning finished


Human asks


Robot enters Robot enters asking modeasking mode



Robot Human



Human asks


Human starts Human starts teachingteaching



Robot Human


Yes

Human teaches


Robot Human

I give you question-answer patternYes

Human teaches


Robot Human

Question pattern:

What book Smith wrote?Yes

Human teaches

Robot Human

Answer pattern:

Smith wrote book “Automata Theory”

Yes

Human teaches



Robot Human

Checking question:

What book wrote Smith?Yes

Human teaches


Robot Human

Checking question:

What book wrote Smith?Smith wrote book “Automata Theory”

Human teaches


Robot Human

I give you question-answer patternYes

Human teaches


Robot Human

Question pattern:

Where is room of Lee?Yes

Human teaches


Robot Human

Answer pattern:

Lee is in room 332Yes

Human teaches

Human-Checking what robot Human-Checking what robot learnedlearned

Robot Human

Lesson finished


Human teaches“Lesson finished”


Robot Human

Lesson finished



What can I do for you?


Robot Human

Question



What can I do for you?


Robot Human

Question





Robot Human


Human asks



Robot Human

Try to guess.

Human asks



Robot Human

Try to guess.

Human asks

Lee wrote book “Automata Theory”

Observe that robot found similarity between Smith and Lee and generalized (incorrectly)

How we linkedHow we linked Behavior, Dialog Behavior, Dialog and Learningand Learning

• The dialog/behavior has the following components: – (1) Eliza-like natural language dialogs based on pattern

matching and limited parsing. • Commercial products like Memoni, Dog.Com, Heart, Alice,

and Doctor all use this technology, very successfully – for instance Alice program won the 2001 Turing competition.

– This is a “conversational” part of the robot brain, based on pattern-matching, parsing and black-board principles.

– It is also a kind of “operating system” of the robot, which supervises other subroutines.

• (2) Subroutines with logical data base and natural language parsing (CHAT). – This is the logical part of the brain used to find connections between

places, timings and all kind of logical and relational reasonings, such as answering questions about Japanese geography.

• (3) Use of generalization and analogy in dialog on many levels. – Random and intentional linking of spoken language, sound effects and facial gestures.

– Use of Constructive Induction approach to help generalization, analogy reasoning and probabilistic generations in verbal and non-verbal dialog, like learning when to smile or turn the head off the partner.

Behavior, Dialog and LearningBehavior, Dialog and Learning

• (4) Model of the robot, model of the user, scenario of the situation, history of the dialog, all used in the conversation.

• (5) Use of word spotting in speech recognition rather than single word or continuous speech recognition.

• (6) Avoidance of “I do not know”, “I do not understand” answers from the robot. – Our robot will have always something to say, in the worst case,

over-generalized, with not valid analogies or even nonsensical and random.

Behavior, Dialog and LearningBehavior, Dialog and Learning

- - -

00 01 11 10

00 - - -01 - - -11 - – 1,1,1,0 -10 -

ABABCDCD

0,0,0,3

-

Input Variables

A: 0=what, 1=where, B: 0=wrote, 1=is, C: 0=book, 1=room, D: 0=Smith, 1=Lee

0000=what wrote book Smith?

0111=what is room Lee?

1111=where is room Lee?

Example Answer = Smith wrotebook “Automata Theory”

Example Answer = Lee is room 332

New Question:

0001: What wrote book Lee?

Fig. 3. Question Answering by induction of answer parameters.

Output Variables

X: 0=Smith, 1=Lee, 2=Perkowski, Y: 0=wrote , 1=is, Z: 0=book, 1=room, 2=building, V: 0=332, 1=73, 2=245, 3=“Automata Theory”, 4=“Logic Design”

X,Y,Z,V

C - right light sensorC - right light sensor

D - left microphoneD - left microphone

A - rightA - rightmicrophonemicrophoneB - left light sensorB - left light sensor00 01 11 10

00 - 1,0 -01 2,0 1,0 1,111 - – 0,0 -10 - 0,0 - -

ABAB

CDCD

-0,0

Head_Horiz , Eye_Blink

Robot turnshead right,away fromlight in left

Robot turns head leftwith equal front lightingand no sound.

It blinks eyes

Robot doesnothing

Robot turns headleft, away from lightin right, towardssound in left

Fig. 2. Seven examples (4-input, 2 output minterms) aregiven by the teacher as correct robot behaviors

Generalization of Generalization of the Ashenhurst-the Ashenhurst-

Curtis Curtis decomposition decomposition

modelmodel

This kind of tables known from This kind of tables known from Rough Sets, Decision Trees, etc Rough Sets, Decision Trees, etc Data MiningData Mining

Decomposition is hierarchicalAt every step many decompositions exist

Constructive Induction: Constructive Induction: Technical DetailsTechnical Details

• U. Wong and M. Perkowski, A New Approach to Robot’s Imitation of Behaviors by Decomposition of Multiple-Valued Relations, Proc. 5th Intern. Workshop on Boolean Problems, Freiberg, Germany, Sept. 19-20, 2002, pp. 265-270.

• A. Mishchenko, B. Steinbach and M. Perkowski, An Algorithm for Bi-Decomposition of Logic Functions, Proc. DAC 2001, June 18-22, Las Vegas, pp. 103-108.

• A. Mishchenko, B. Steinbach and M. Perkowski, Bi-Decomposition of Multi-Valued Relations, Proc. 10th IWLS, pp. 35-40, Granlibakken, CA, June 12-15, 2001. IEEE Computer Society and ACM SIGDA.

• Decision Trees, Ashenhurst/Curtis hierarchical decomposition and Bi-Decomposition algorithms are used in our software

• These methods create our subset of MVSIS system developed under Prof. Robert Brayton at University of California at Berkeley [2].– The entire MVSIS system can be also used.

• The system generates robot’s behaviors (C program codes) from examples given by the users.

• This method is used for embedded system design, but we use it specifically for robot interaction.

Constructive InductionConstructive Induction

Additional Slides with Background

Ashenhurst Functional DecompositionAshenhurst Functional DecompositionEvaluates the data function and attempts to

decompose into simpler functions.

if A B = , it is disjoint decomposition

if A B , it is non-disjoint decomposition

B - bound set

A - free set

F(X) = H( G(B), A ), X = A F(X) = H( G(B), A ), X = A B B

X

A Standard Map of A Standard Map of function ‘z’function ‘z’

Bound Set

Fre

e S

et

a b \ c

z

Columns 0 and 1and

columns 0 and 2are compatible

column compatibility = 2

Explain the concept of Explain the concept of generalized don’t caresgeneralized don’t cares

NEW Decomposition of Multi-Valued NEW Decomposition of Multi-Valued RelationsRelations

if A B = , it is disjoint decomposition

if A B , it is non-disjoint decomposition

F(X) = H( G(B), A ), X = A B

Relation Rel

atio

n

Rel

atio

n

A

B

X

Forming a CCG from a K-MapForming a CCG from a K-Map

z

Bound Set

Fre

e S

et

a b \ cColumns 0 and 1 and columns 0 and 2 are compatiblecolumn compatibility index = 2

C1

C2

C0

Column Compatibility

Graph

Forming a CIG from a K-MapForming a CIG from a K-MapColumns 1 and 2 are incompatiblechromatic number = 2

z

a b \ c

C1

C2

C0

Column Incompatibility Graph

• A unified internal language is used to describe behaviors in which text generation and facial gestures are unified.

• This language is for learned behaviors.

• Expressions (programs) in this language are either created by humans or induced automatically from examples given by trainers.

Constructive InductionConstructive Induction

The integrated approach to robot vision and speech based dialogs

Open CV image processing Open CV image processing software from Intel software from Intel

Hidden Hidden Markov Model Markov Model

Based Face Based Face RecognitionRecognition

Hidden Markov Model Based Hidden Markov Model Based Face RecognitionFace Recognition

Braitenberg Braitenberg Vehicles Vehicles

• Two sensors two motors

• Many behaviors from simple rules

• Control can be a combinational mapping or automaton

• Combinational logic can be binary, multiple-valued, fuzzy and quantum.

• Automaton can be binary, multiple-valued, fuzzy, probabilistic, non-deterministic or quantum (entangled)

• This is a new concept in Machine Learning and Robotics

AND QUANTUM BREITENBERG FACESAND QUANTUM BREITENBERG FACES

Hadamard gateHadamard gate

In standard computers probabilistic components are expensive and have aliasing. In quantum these are the cheapest gates and they are ideal random number generators.

Square Root of NOTSquare Root of NOT

NOT NOT

|0 |1

Superposition state V0 observed as random state

(a)

V

a

b

P

Q

1 0 0 0 0 1 0 0 0 0 1+i 1-i 0 0 1-i 1+i

1/2(b) (c)

(e)(d)V+

a

b

P

Q

1 0 0 0 0 1 0 0 0 0 1-i 1+i 0 0 1+i 1-i

1/2

Deterministic behaviors can be composed from probability waves. This does not happen outside quantum world.

Square root controlled gate and its matrix

Square root controlled hermitian and its matrix

Two Square root gates composed to an inverter

=0.5 + 0.5i 0.5 – 0.5i0.5 – 0.5i 0.5 + 0.5i

0.5 + 0.5i 0.5 – 0.5i0.5 – 0.5i 0.5 + 0.5i

=1 + i 1 –i1 –i 1 + i

1 + i 1 –i1 –i 1 + i

= 0.5 0.5

(1 + i)(1 + i)+(1 - i)(1 - i) (1 + i)(1 - i)+(1 - i)(1 + i)(1 - i)(1 + i)+(1 + i)(1 - i) (1 - i)(1 - i)+(1 + i)(1 + i)= 0.25

(1 + 2i + i2)+(1 - 2i + i2) 2(1 - i2)2(1 - i2) (1 + 2i + i2)+(1 - 2i + i2)= 0.25 =

=

1 + i2 + 1 + i2 2 - 2i2

2 - 2i2 1 + i2 + 1 + i2= 0.25=

1 + i2 1 - i2

1 - i2 1 + i2= 0.5

1 + i2 1 - i2

1 - i2 1 + i2= 0.5 =1 + (-1) 1 - (-1) 1 - (-1) 1 + (-1)0.5 =

0 2 2 00.5

0 1 1 0=

Analysis of a Analysis of a Quantum Circuit. Quantum Circuit.

Matrices in Hilbert Matrices in Hilbert SpaceSpace

Kronecker Product for Kronecker Product for Quantum Circuit Analysis Quantum Circuit Analysis

Parallel connection of gates requires Kronecker Product.

Serial connection of gates (Previous slides) requires standard matrix multiplication.

Analysis of a Quantum CircuitAnalysis of a Quantum Circuit

Conclusion. What did we learnConclusion. What did we learn

• (1) the more degrees of freedom the better the animation realism.

• (2) synchronization of spoken text and head (especially jaw) movements are important but difficult.

• (3) gestures and speech intonation of the head should be slightly exaggerated.

Conclusion. What did we learn(cont)Conclusion. What did we learn(cont)

• (4) the sound should be laud to cover noises coming from motors and gears and for a better theatrical effect.

• (5) noise of servos can be also reduced by appropriate animation and synchronization.

• (6) best available ATR and TTS packages should be applied, especially those that use word spotting.

• (7) use puppet theatre experiences.

• (8) because of a too slow learning, improved parameterized learning methods will be developed, but also based on constructive induction.

• (9) open question: funny versus beautiful.• (10) either high quality voice recognition from headset or low

quality in noisy room. YOU CANNOT HAVE BOTH WITH CURRENT ATR TOOLS.

• The bi-decomposer of relations and other useful software used in this project can be downloaded from http://www-cad.eecs.berkeley.edu/mvsis/.

Conclusion. What did we learn(cont)Conclusion. What did we learn(cont)

ConclusionConclusionMonday, May 10, Intelligent Robotics Laboratory and Industrial Robotics Laboratory. Demo 10am- 2pm

Thursday, May 13, Convention Center. Demo 10am- 2pm

Sunday, June 6, PSU Balroom Smith Center. Competition and Demo 10am- 2pm

Help needed. If you can program, interface PCs, know about networks, want to help with WWW Page, build robots, learn advanced theories, ……. You are welcome to the lab.

Probabilistic State Machines to describe emotions Happy state Ironic state Unhappy state “you are...

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